{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df_day = pd.read_csv('Bike-Sharing-Dataset/day.csv')\n",
    "df_day.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_day.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hour = pd.read_csv('Bike-Sharing-Dataset/hour.csv')\n",
    "df_hour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(df_day[\"casual\"], bins = 30, kde = True)\n",
    "plt.xlabel(\"casual\",fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(df_day[\"registered\"], bins = 30, kde = True)\n",
    "plt.xlabel(\"registered\",fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(df_day[\"cnt\"], bins = 30, kde = True)\n",
    "plt.xlabel(\"cnt\",fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a74a050>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(1,2,sharey = True, figsize = (6,4))\n",
    "sns.boxplot(data = df_day[\"casual\"], ax = axes[0])\n",
    "#结合了箱型图和核密度估计的特点，连续变量数据的分布并进行比较\n",
    "sns.violinplot(data = df_day[\"casual\"], ax = axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b734a10>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(1,2,sharey = True, figsize = (6,4))\n",
    "sns.boxplot(data = df_day[\"registered\"], ax = axes[0])\n",
    "#结合了箱型图和核密度估计的特点，连续变量数据的分布并进行比较\n",
    "sns.violinplot(data = df_day[\"registered\"], ax = axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b887310>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,axes = plt.subplots(1,2,sharey = True, figsize = (6,4))\n",
    "sns.boxplot(data = df_day[\"cnt\"], ax = axes[0])\n",
    "#结合了箱型图和核密度估计的特点，连续变量数据的分布并进行比较\n",
    "sns.violinplot(data = df_day[\"cnt\"], ax = axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b9c85d0>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = df_day.columns\n",
    "data_corr = df_day.corr()  # 相关系数大于0.5为强相关\n",
    "sns.heatmap(data_corr,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weathersit and temp - 0.99\n",
      "casual and registered - 0.95\n",
      "instant and season - 0.87\n",
      "dteday and yr - 0.83\n",
      "windspeed and registered - 0.67\n",
      "instant and casual - 0.66\n",
      "temp and registered - 0.63\n",
      "instant and registered - 0.63\n",
      "weathersit and registered - 0.63\n",
      "season and casual - 0.59\n",
      "workingday and atemp - 0.59\n",
      "season and registered - 0.57\n",
      "temp and casual - 0.54\n",
      "temp and windspeed - 0.54\n",
      "weathersit and windspeed - 0.54\n",
      "weathersit and casual - 0.54\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "corr_list = []\n",
    "size = data_corr.shape[0]\n",
    "for i in range(0,size):\n",
    "    for j in range(i+1,size):\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j]<1):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j])\n",
    "s_corr_list = sorted(corr_list,key = lambda x: -abs(x[0]))\n",
    "for v,i,j in s_corr_list:\n",
    "    print(\"%s and %s - %.2f\" % (cols[i], cols[j],v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'weekday')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df_day['weekday'])\n",
    "plt.xlabel('weekday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'workingday')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df_day['workingday'])\n",
    "plt.xlabel('workingday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'holiday')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df_day['holiday'])\n",
    "plt.xlabel('holiday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   holiday  weekday  workingday  weathersit      temp     atemp       hum  \\\n",
       "0        0        6           0           2  0.344167  0.363625  0.805833   \n",
       "1        0        0           0           2  0.363478  0.353739  0.696087   \n",
       "2        0        1           1           1  0.196364  0.189405  0.437273   \n",
       "3        0        2           1           1  0.200000  0.212122  0.590435   \n",
       "4        0        3           1           1  0.226957  0.229270  0.436957   \n",
       "\n",
       "   windspeed  \n",
       "0   0.160446  \n",
       "1   0.248539  \n",
       "2   0.248309  \n",
       "3   0.160296  \n",
       "4   0.186900  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_casual = df_day['casual']\n",
    "y_registered = df_day['registered']\n",
    "y_cnt = df_day['cnt']\n",
    "x = df_day.drop(['instant','casual','registered','cnt','dteday','yr','mnth','season'],axis = 1)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对所有y求log\n",
    "y_log_casual = np.log1p(y_casual)\n",
    "y_log_registered = np.log1p(y_registered)\n",
    "y_log_cnt = np.log1p(y_cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weathersit_1  weathersit_2  weathersit_3\n",
       "0             0             1             0\n",
       "1             0             1             0\n",
       "2             1             0             0\n",
       "3             1             0             0\n",
       "4             1             0             0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#/天气/holiday/weekday/workingday 进行独热\n",
    "x['weathersit'].astype(\"object\")\n",
    "x_weathersit_cat = x['weathersit']\n",
    "x_weathersit_cat = pd.get_dummies(x_weathersit_cat,prefix='weathersit')\n",
    "print(type(x_weathersit_cat))\n",
    "x_weathersit_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday_0</th>\n",
       "      <th>holiday_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   holiday_0  holiday_1\n",
       "0          1          0\n",
       "1          1          0\n",
       "2          1          0\n",
       "3          1          0\n",
       "4          1          0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['holiday'].astype(\"object\")\n",
    "x_holiday_cat = x['holiday']\n",
    "x_holiday_cat = pd.get_dummies(x_holiday_cat,prefix='holiday')\n",
    "print(type(x_holiday_cat))\n",
    "x_holiday_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weekday_0  weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6\n",
       "0          0          0          0          0          0          0          1\n",
       "1          1          0          0          0          0          0          0\n",
       "2          0          1          0          0          0          0          0\n",
       "3          0          0          1          0          0          0          0\n",
       "4          0          0          0          1          0          0          0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['weekday'].astype(\"object\")\n",
    "x_weekday_cat = x['weekday']\n",
    "x_weekday_cat = pd.get_dummies(x_weekday_cat,prefix='weekday')\n",
    "print(type(x_weekday_cat))\n",
    "x_weekday_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workingday_0</th>\n",
       "      <th>workingday_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   workingday_0  workingday_1\n",
       "0             1             0\n",
       "1             1             0\n",
       "2             0             1\n",
       "3             0             1\n",
       "4             0             1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['workingday'].astype(\"object\")\n",
    "x_workingday_cat = x['workingday']\n",
    "x_workingday_cat = pd.get_dummies(x_workingday_cat,prefix='workingday')\n",
    "print(type(x_workingday_cat))\n",
    "x_workingday_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed\n",
       "0  0.344167  0.363625  0.805833   0.160446\n",
       "1  0.363478  0.353739  0.696087   0.248539\n",
       "2  0.196364  0.189405  0.437273   0.248309\n",
       "3  0.200000  0.212122  0.590435   0.160296\n",
       "4  0.226957  0.229270  0.436957   0.186900"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x.drop(['weathersit','weekday','holiday','workingday','weathersit'],axis = 1)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['temp', 'atemp', 'hum', 'windspeed'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "feat_names = x.columns\n",
    "print(feat_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss_x = StandardScaler()\n",
    "x = ss_x.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>weathersit_1</th>\n",
       "      <th>weathersit_2</th>\n",
       "      <th>weathersit_3</th>\n",
       "      <th>holiday_0</th>\n",
       "      <th>holiday_1</th>\n",
       "      <th>workingday_0</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>casual</th>\n",
       "      <th>log_casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>log_registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.826662</td>\n",
       "      <td>-0.679946</td>\n",
       "      <td>1.250171</td>\n",
       "      <td>-0.387892</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>331</td>\n",
       "      <td>5.805135</td>\n",
       "      <td>654</td>\n",
       "      <td>6.484635</td>\n",
       "      <td>985</td>\n",
       "      <td>6.893656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.721095</td>\n",
       "      <td>-0.740652</td>\n",
       "      <td>0.479113</td>\n",
       "      <td>0.749602</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>4.882802</td>\n",
       "      <td>670</td>\n",
       "      <td>6.508769</td>\n",
       "      <td>801</td>\n",
       "      <td>6.687109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.634657</td>\n",
       "      <td>-1.749767</td>\n",
       "      <td>-1.339274</td>\n",
       "      <td>0.746632</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>1229</td>\n",
       "      <td>7.114769</td>\n",
       "      <td>1349</td>\n",
       "      <td>7.207860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-1.614780</td>\n",
       "      <td>-1.610270</td>\n",
       "      <td>-0.263182</td>\n",
       "      <td>-0.389829</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>4.691348</td>\n",
       "      <td>1454</td>\n",
       "      <td>7.282761</td>\n",
       "      <td>1562</td>\n",
       "      <td>7.354362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.467414</td>\n",
       "      <td>-1.504971</td>\n",
       "      <td>-1.341494</td>\n",
       "      <td>-0.046307</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>4.418841</td>\n",
       "      <td>1518</td>\n",
       "      <td>7.325808</td>\n",
       "      <td>1600</td>\n",
       "      <td>7.378384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       temp     atemp       hum  windspeed  weathersit_1  weathersit_2  \\\n",
       "0 -0.826662 -0.679946  1.250171  -0.387892             0             1   \n",
       "1 -0.721095 -0.740652  0.479113   0.749602             0             1   \n",
       "2 -1.634657 -1.749767 -1.339274   0.746632             1             0   \n",
       "3 -1.614780 -1.610270 -0.263182  -0.389829             1             0   \n",
       "4 -1.467414 -1.504971 -1.341494  -0.046307             1             0   \n",
       "\n",
       "   weathersit_3  holiday_0  holiday_1  workingday_0  ...  weekday_3  \\\n",
       "0             0          1          0             1  ...          0   \n",
       "1             0          1          0             1  ...          0   \n",
       "2             0          1          0             0  ...          0   \n",
       "3             0          1          0             0  ...          0   \n",
       "4             0          1          0             0  ...          1   \n",
       "\n",
       "   weekday_4  weekday_5  weekday_6  casual  log_casual  registered  \\\n",
       "0          0          0          1     331    5.805135         654   \n",
       "1          0          0          0     131    4.882802         670   \n",
       "2          0          0          0     120    4.795791        1229   \n",
       "3          0          0          0     108    4.691348        1454   \n",
       "4          0          0          0      82    4.418841        1518   \n",
       "\n",
       "   log_registered   cnt   log_cnt  \n",
       "0        6.484635   985  6.893656  \n",
       "1        6.508769   801  6.687109  \n",
       "2        7.114769  1349  7.207860  \n",
       "3        7.282761  1562  7.354362  \n",
       "4        7.325808  1600  7.378384  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe_data = pd.DataFrame(data=x,columns = feat_names,index = df_day.index)\n",
    "fe_data = pd.concat([fe_data,x_weathersit_cat,x_holiday_cat,x_workingday_cat,x_weekday_cat],axis = 1,ignore_index=False)\n",
    "fe_data['casual'] = y_casual\n",
    "fe_data['log_casual'] = y_log_casual\n",
    "fe_data['registered'] = y_registered\n",
    "fe_data['log_registered'] = y_log_registered\n",
    "fe_data['cnt'] = y_cnt\n",
    "fe_data['log_cnt'] = y_log_cnt\n",
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "fe_data.to_csv('Bike-Sharing-Dataset/FE_day.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
